For any machine learning company, the technical content you create can make or break your acquisition funnels. For highly specialized products like deep learning frameworks or data science tooling, explaining to your users what correctly your service is doing and how it helps them get started with your product quickly. Users that get started rapidly then go on to become more successful with your product in the long term. At the same time, teaching the basics of machine learning and data science to your audience can help establish your company as a thought leader in the industry.
If you are working on a machine learning blog or documentation site and are looking for inspiration, this article is for you. We’ve collected the resources on machine learning that stand out due to unique content and unique approaches to explaining highly technical topics.
List of ten machine learning content sites
We divided our list into four categories by type of content the resources publish:
- Overview and key concepts — websites that focus on high-level, accessible content.
- Deep dives — in-depth guides and blog posts.
- Implementation and competition — sites where you can implement machine learning algorithms and see how they compare to the algorithms others have written.
- News and curiosities of machine learning.
Overviews and Key Concepts
Jason Brownlee, the owner of this website, has a Master’s and a Ph.D. degree in Artificial Intelligence, and he has worked on machine learning systems for defense, startups, and severe weather forecasting.
This website’s top-down approach focuses on working through a dataset end-to-end and getting a result with popular platforms like Scikit-learn, R, and Keras.
This website is excellent for beginners because it helps you get straight to using programming to do ML instead of reading about concepts and theories.
Our favourite read: The getting started guide is probably the best guide to get started in the Machine Learning world. This guide offers resources sorted by level, which is also useful if you already know the basics.
Most of the articles on this resource are in the form of a case with code and drawings. The text is rigorous and logical, and the writing style is very friendly to beginners in data science and machine learning.
Keep in mind that the quality of writing can differ from one article to the next. It can sometimes be hard to find the exact content you are looking for since the website does not have a search engine, but you can use Google or DuckDuckGo with a site:towardsdatascience.com prefix to work around this limitation.
Our favourite guide: The Ultimate Guide to Getting Started in Data Science.
For audiences that don’t like long, technically dense articles but still want to learn about machine learning, this YouTube channel offers a broad base of high-quality content. The channel is especially interesting because you can find anything from reviews of mathematical and statistical concepts to interesting implementation examples.
Our favourite video: 7 Ways to Make Money with Machine Learning.
This blog has well-structured short articles that are informative and educational. The content here is a good fit for more advanced readers. Some materials can be hard to follow for those not familiar with the Data Science and Machine Learning concepts.
Our favourite post: Why do you need to improve your training data and how to do it.
Domino Data Lab makes tooling for machine learning development, and its resources cover a range of topics from the basics to advanced concepts. The content is easy to understand. Try using the search engine on the page to look up the posts on the topics relevant to you.
Our favourite story: Data Science at The New York Times
This website skips the non-essential and instead takes you directly to applying Data Science and Machine Learning in professional environments.If you’re a developer, analyst, manager, or an aspiring data scientist looking to learn more about data science and machine learning, this is a great blog to read through .
Our favourite guide: How to Become a Data Scientist in 2019 (Hadouken!)
Implementation and Competition
#7: Analytics Vidhya
AV is excellent for those looking for a platform to compete in hackathons and to practice their DS skills. The blogs and articles are also informative and fun to read.
AV has several learning paths that provide clear directions on using various tools and techniques in competitions. AV’s very own DS adaptive test, DSAT, is perfect for those looking to test their machine learning skills and to practice daily. Some competitions are free to join.
Kaggle is a platform that includes the tools to share and collaborate on data science projects. Its new feature, notebooks, is an excellent way for anyone to get started with data science: you can see how others structure their data science research and learn by following other users’ examples. The notebooks, powered by Jupyter Notebooks, work in your browser and include free GPU compute so that you can run your machine learning models quickly.
The Kaggle platform includes competitions where you can compare your algorithms to those of other participants on the platform.
Our favourite competition story: Two Sigma Financial Modeling Code Competition, 5th Place Winners’ Interview
News and Recent Research
Google News provides a way to see the news from all over the internet on a single page — and it works just as well for Data Science as for other topics. Find inspiration for data science and machine learning articles from tutorials and blogs to industry news and startups. You can read myriads of articles from Forbes, TechRepublic, Infoworld, and other reputable sources.
Nature is one of the most prestigious science magazines worldwide. Don’t miss the Nature Machine Learning blog with its high-quality content that’s reviewed by experts before it’s published. You will need to be familiar with the basics of machine learning to follow most articles published here as the content is often quite technical.
Machine learning and data science may seem like complex topics, but the examples from this article illustrate that it’s possible to explain the complex in an engaging and authentic way.If you’re interested in creating your own machine learning content but don’t know how to start, contact us today.